Enterprise Data Architect

Summary We are looking for an Enterprise Data Architect who combines long-term vision, executive influence, governance discipline and real technical depth. The ideal candidate can shape the enterprise direction for data and BI, create clarity across a complex modern platform landscape, and then work directly with delivery teams to make the architecture real.

The Enterprise Data Architect defines and steers the long-term enterprise data analytics architecture. The role connects business strategy, governance, information architecture and modern platform engineering. The successful candidate can explain complex data concepts in clear C-level language, while remaining sufficiently hands-on to challenge designs, review code or configurations, prototype solutions and work credibly with developers, data engineers, BI specialists, security experts and business data owners.

Responsibilities

Create and maintain multi-year data analytics supporting AI company strategy aligned with business growth, profitability, operational efficiency, regulatory obligations and technology modernization. Able to put into practice DAMA-DMBOK / TOGAF principles. Target architecture and roadmaps: Define current-state, transition and target architecture across data platforms, integration, storage, processing, semantic layers, BI, governance, metadata, data quality and information security.

Executive advisory: Translate architecture choices, risks, dependencies and investment needs into concise, business-oriented recommendations for C-level executives, steering committees and senior stakeholders. Architecture governance: Establish architecture principles, standards, reference patterns, decision records and review mechanisms.

Ensure projects comply with the target architecture while allowing justified exceptions. Hands-on technical leadership : Run design workshops, review data models and technical designs, inspect SQL, notebooks, pipelines, APIs, security models and platform configurations, and build focused proofs of concept when needed.

Business and technology alignment: Bridge business domains, data owners, platform teams, BI teams, AI teams, enterprise architecture, security, privacy, legal, procurement and delivery partners. Technology portfolio rationalization: Clarify the role and boundaries of Snowflake, Databricks, Microsoft Fabric, Power BI, Qlik and other enterprise data capabilities.

Reduce overlap, unmanaged proliferation, cost and vendor lock-in. Delivery assurance: Provide architecture support from ideation through implementation, migration and operational handover, including non-functional requirements, cost, resilience, observability and supportability.